Objective: The objective of this study is to determine the incidence of impacted teeth and the frequency of pathologies they caused by cone beam computed tomography (CBCT) retrospectively. Materials and Methods: In this study, 608 patients' CBCT images were analyzed retrospectively. Detected impacted teeth were classified as incisor, canine, premolar, molar, third molar, and supernumerary teeth. The pathologies caused by impacted teeth are classified as cysts or tumors, tooth decay, root resorptions, and periodontal bone loss. Results: Impacted teeth were detected in 34.37% of the 608 CBCT images included in the study. The distribution of impacted teeth was 9.4% incisor, 29.4% canine, 9.9% premolar, 2.9% molar, 9.3% supernumerary, and 39.9% third molar teeth. Approximately 63.7% of the impacted teeth caused a pathology. The pathology that was most commonly caused by impacted teeth was periodontal bone loss (44.4%), and respectively others were root resorptions (33.3%), cysts or tumors (8.6%), and tooth decay (2.3%). The most common cause of this pathology was right mandibular third molar teeth. Conclusion: Impacted teeth were common and they often caused a pathology. CBCT is a useful device to assess the impacted teeth. When the impacted teeth are evaluated, each tooth should be assessed within itself. If the impacted teeth are not caused by pathology, they can be kept under control.

Background: One of the primary concerns in the paediatric emergencies is traumatic dental injuries.Objective: This study aimed to create trauma in primary teeth and reveal its effects finite element analysis.Design: Three‐dimensional models were created using cone‐beam computed tomography images, representing a maxillary primary central incisor. An impact force moving at a speed of 10 m/s was simulated on the labial tooth surface in two directions: buccal and incisal.Results: The stress and deformation experienced in the adjacent tooth due to the primary tooth were higher than those generated in the permanent tooth. Forces applied in the incisal direction resulted in higher levels of stress and deformation in the permanent tooth germ. The difference between the stress and deformation values in primary teeth in the forces applied in the buccal and incisal directions is 21% and 75%, respectively; in the permanent tooth germ, this difference was 233% and 100%, respectively.Conclusions: Based on the findings of this study, it is crucial to thoroughly evaluate not only the affected primary tooth but also the adjacent teeth and the permanent tooth germ in traumatic dental injuries. This comprehensive examination allows for the anticipation and management of potential long‐term problems.

Zygomatic implants are becoming an ideal treatment approach for implant-supported prosthesis treatment developed for the atrophic maxilla. This study aims to evaluate the amount and distribution of stress in implants and peri-implant bone using different implant-supported prosthesis configurations in Aramany Class I maxillary defects through 3-dimensional finite element analysis. A 3-dimensional finite element model of the Aramany class I defect was created. Three different implant-supported prostheses were modelled: model 1: 1 zygomatic implant and 3 dental implants, model 2: 1 zygomatic implant and 2 dental implants, and model 3: 2 zygomatic implants. Vertical and horizontal loads of 150 N were applied in 6 different ways to the defected and non-defective areas. Maximum principal stress and von mises stresses in the bone surrounding the implants were evaluated. When all loading conditions were evaluated with both porcelain layer on Co-Cr framework and acrylic layer on acrylic framework, the highest maximum principal stress value was observed in Model 3. In contrast, the lowest value was observed in Model 1. The highest maximum principal stress occurred when a horizontal load was applied simultaneously to both the defective and non-defective areas. In contrast, the lowest value was observed when a vertical load was applied to the non-defective area. The von Mises stress values were found to be similar across all models when both restoration materials were used. Based on the results of this study, it can be concluded that increasing the number of implants in the non-defective area reduces the highest stress value while using acrylic as a restoration material slightly increases the stress value.

Objective: The aim of this study is to evaluate the relationship between anatomical reference points used during orthognathic surgery and to minimize the risks of iatrogenic neurovascular damage. Materials and methods: This retrospective study included cone-beam computed tomography (CBCT) images involving the mandible from patients who visited Recep Tayyip Erdoğan University Faculty of Dentistry between January 2018 and September 2023. The age range of the included individuals was set between 18 and 80 years. Horizontal and vertical distances between mandibular anatomical structures, such as the lingula mandibula (LM), mandibular foramen (MF), antilingula (AL), and surrounding structures were measured using CBCT software. Individuals with intraosseous pathology, insufficient image quality, or a history of surgical/orthodontic treatment were excluded from the study. Results: A total of 240 hemimandibles from 120 patients were analyzed (55.83% female, 44.17% male; mean age: 46.78 ± 15.30 years). Significant differences were identified in LM positions according to different AL types. The LM was found to be more inferior and posterior relative to hill and ridge type ALs, while it was more anterior relative to plateau type ALs. In 26.25% of mandibular rami, AL was not detected. Conclusion: The position of the AL can serve as a guide in determining the osteotomy line during inferior vertical ramus osteotomy (IVRO). However, relying solely on AL as a reference point may increase the risk of inferior alveolar nerve (IAN) injury. Preoperative tomographic evaluations to determine the relationships among LM, MF, and AL can provide a safer approach in surgical planning, reduce complications, and help protect neurovascular structures.

Objectives: To evaluate the diagnostic performance of a YOLOv8x-based deep learning model for detecting approximal, occlusal and buccal caries on paediatric panoramic radiographs and to compare its performance with human observers with different levels of clinical experience. Methods: A total of 1526 panoramic radiographs obtained from children aged 5–12 years were retrospectively analysed. Approximal, occlusal, and buccal caries in primary molars were annotated and used to train a YOLOv8x object-detection model. Model performance was evaluated on an independent test set and compared with three human observers: an intern dentist (ID), a novice specialist student (NSS), and an experienced specialist student (ESS). Diagnostic performance was assessed using precision, sensitivity, F1 score, and true positive counts. Results: The YOLOv8x model demonstrated moderate performance in detecting approximal caries (F1 score: 0.576) but showed limited performance for occlusal caries (F1 score: 0.24) and failed to detect buccal caries. The AI model showed lesion-dependent performance. For approximal caries, it performed comparably to ESS observers (p > 0.05) and better than ID (p < 0.001). Performance was poor for buccal caries (p < 0.001), and intermediate for occlusal caries, with no difference from NSS or ESS (p > 0.05) but lower than ID (p < 0.001). Overall, performance was comparable to experienced observers (p > 0.05) and superior to less experienced observers (p < 0.001). Conclusions: The YOLOv8x model achieved diagnostic performance comparable to less experienced clinicians in detecting dental caries on paediatric panoramic radiographs but did not reach expert-level accuracy. These findings suggest that deep learning models may serve as supportive tools in panoramic caries assessment rather than replacements for expert interpretation.